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Beyond the Boxscore
The Cubs FCI (1991 — 2004)
See the Full Table
MLB Average FCI (1991-2004)
See the Full Table
* — In 2001, TMR did not report % changes in ticket prices or FCI. I calculated the numbers, but I suspect that TMR may have redefined their method of calculating average ticket price (and thus FCI) in 2001.
† — TMR does not note the Team's Won-Loss Record, but I feel that this is an important factor to see alongside ticket price changes.
° — In 1994, a labor dispute caused the season to end August 12. The strike and subsequent lockout caused most MLB teams to play about 18 fewer games in 1995.
‡ — In 1998, the Cubs played 163 regular season games. They played a single playoff game against the San Francisco Giants to determine the National League Wild Card. The Cubs won, and played three games against the Atlanta Braves. In 2003, the Cubs won the NL Central Division. They won a five game NL Division Series against the Atlanta Braves. The Cubs then lost in seven games during the NL Championship Series to the Florida Marlins. Playoff games are not included in the Cub's records.
CPI data from: Bureau of Labor Statistics CPI Data.
GDP Data from: Commerce Department Bureau of Economic Analysis. Download the spreadsheet of GDP data.
Cubs Payroll information used from USA Today's Baseball Salary Database.
Cubs Attendance information used from Cubs Year by Year Results.
Because the Cubs (and all other major league teams) set their ticket prices at the end of the year for which they take effect, I have adjusted the data in my calculations by setting ticket dates back one year.
For example: my model of Chicago Cubs 2004 ticket prices is based off of the following factors:
The first step in building my model has been to find the correlation between Cubs ticket prices and each individual variable.
Perhaps the most interesting number here is also the most obvious number for Cubs fans. The Cubs ticket prices have very little to do with whether the Cubs win or not.
The interpretation of the above table is this: "coefficient of determination" percent of changes in Cubs ticket prices are accounted for by changes in the independent variable. Thus, we would say that the 95.36% of the change in Cubs ticket price over the period from 1991-2004 can be accounted for by examining changes in the Cubs Payroll. From a business standpoint, this seems logical as ticket revenues are the Cubs most significant form of revenue, and Cubs payroll is the most significant expense.
Using Microsoft Excel, I have constructed a multiple regression analysis using Cubs ticket prices from 1991-2004 as the dependent variable, and the above eight variables as the independent variables. I am presenting this work as an initial model now, but will refine it over time.
The model is this:
Cubs ticket price = -7.199 + (11.095 * Cubs win %) + (8.538E-08 * Cubs Payroll) + (0.552 * White Sox Tickets) + (.538 * MLB Avg Ticket Price) + (-1.221E-06 * Avg. Attendance) + (-0.004 * CPI) + (-0.0004 * GDP) + (0.0001764 * total attendance)
Using this model, which has a (coefficient of determination of 99.2%) I have calculated the Cubs projected ticket price for each of the previous 14 years. I have also listed the actual ticket price, and the deviation from the model.
Obviously, there are some glitches in this model because there are negative components (CPI, GDP, and average attendance). One of the causes, I believe is that when performing a multiple regression analysis the independent variables are assumed to be infact independent. Well, the MLB average ticket price is actually partially dependent on the Cubs, and the White Sox, so I will eliminate it in my next attempt. I also used Cubs attendance in total, and average Cubs attendance per game. I was attempting to account for the strike shortened years, but having both seems to be degrading the quality of my model. As a result, I will eliminate the average attendance per game because it has a lower coefficient of determination. Finally, because the coefficient of determination is only 1.4% for the Cubs winning percent, I will eliminate this variable.
I will run a second multiple regression analysis to determine Cubs ticket prices (dependent variable) using the Cubs Payroll, White Sox average ticket price, Total Home Attendance, CPI, and GDP as the independent variables.
After meeting with a professor of mine (thanks to Rex Cutshall), I decided to remove the GDP level because it had a high correlation with the CPI level. This time, my regression results had a coefficient of correlation of 98.12%. However, the p-values, which are a measure of significance were too large on all of my variables, except the White Sox Average ticket price. Below are the results of the third regression I ran.
Additional Trials & Conclusion
At this point, I tried monkeying around with the different variables to try and strike a balance between model significance, model accuracy, and common sense. What I ended up doing was a procedure vaguely similar to a stepwise regression. In a stepwise regression, you begin looking for the variable with the highest coefficient of determination, and then you find the two variable multiple linear regression with the highest coefficient of determination, and keep adding variables until you add an insignificant variable.
I just started with the average White Sox ticket price, because it has consistently had a low p-value in trials #1-3. I then added the Cubs Payroll and found that both variables were significant (p value below .05). However, when I added in the Cubs attendance, I found that the third variable was statistically insignificant.
So, I will conclude this mess by saying: The best model I care to create of the Cubs Ticket Price relies on the average White Sox ticket price, and the Payroll for the Cubs. These two variables form a model which explains 97.9% of the Average Cubs ticket price, and both variables are significant.
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